Employment Eligibility Statement
Due to specific project and client requirements, this position is open to U.S. Citizens and U.S. Lawful Permanent Residents (s) . Sponsorship is not available at this time.
Danta Technologies evaluates all candidates in compliance with the Immigration and Nationality Act (INA) and EEOC guidelines . All hiring decisions are made without regard to race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other protected characteristic.
Position: Principal Data Architect-Data Modelling
Location: Remote - USA only
Contract: Long term position
Pay Rate: $55/hr on W2/Danta Technologies payroll (OR) $65/hr on C2C
Must-Have Experience
- 15+ years of hands-on data engineering and data architecture experience, with a track record of owning enterprise-scale platforms end to end.
- Deep data modeling expertise — dimensional, normalized, and Data Vault — with real judgment about grain, conformance, and when each pattern applies.
- Proven experience architecting lakehouse and/or data mesh platforms: Databricks, Delta Lake, PySpark, Snowflake, Kafka, Spark Structured Streaming, and cloud-native data services (AWS, Azure).
- Strong command of SQL and performance engineering: query optimization, partitioning and clustering, and cost management at scale.
- Demonstrated ownership of data governance, data quality, master data, metadata, and lineage programs — not just the tooling, but the operating model.
- Experience with data security and compliance in regulated industries (financial services, payments, cybersecurity, healthcare).
- Experience migrating legacy on-prem warehouses and ETL estates to cloud-native platforms.
- Working familiarity with the data requirements of ML and AI systems — feature pipelines, training data curation, and retrieval/vector data layers — enough to architect for them credibly and partner well with AI teams. Technical Skills
- Expert: SQL, Python, PySpark, data modeling, Databricks, Delta Lake, Snowflake, Kafka, Spark Structured Streaming, AWS (S3, Glue, Redshift, Kinesis, EKS, Lambda), Airflow, Terraform, Git-based CI/CD.
- Strong: Azure data services, dbt, data quality frameworks (Great Expectations or equivalent), catalog and lineage platforms (Unity Catalog, Collibra, Alation, Purview), MDM tooling, BI semantic layers (Power BI, Tableau, Looker), Docker, Kubernetes, FastAPI, observability tooling (Grafana, CloudWatch).
- Familiar: MLflow, feature stores, vector databases (OpenSearch, Pinecone, FAISS, ChromaDB), knowledge graphs (Neo4j), LLM APIs and RAG frameworks (LangChain, LlamaIndex, AWS Bedrock, Claude, OpenAI.
QUALIFICATIONS
- BS / MS in Computer Science, Information Systems, Data Engineering, or a related quantitative field.
- Prior experience at a global financial institution (payments, risk, AML, compliance) or large enterprise SaaS, operating under strict regulatory oversight.
- Experience in presales solutioning for large data programs: RFP/RFI responses, SOW shaping, effort estimation, and CXO-level solutioning.
- Certifications such as Databricks Data Engineer Professional, AWS Certified Data Engineer or Solutions Architect, or Snowflake SnowPro Advanced Architect.
- Familiarity with emerging standards for AI and agent data access (e.g., MCP — Model Context Protocol) and agent entitlement models.
Notes:- All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.
Benefits: Danta offers a compensation package to all W2 employees that are competitive in the industry. It consists of competitive pay, the option to elect healthcare insurance (Dental, Medical, Vision), Major holidays and Paid sick leave as per state law.
The rate/ Salary range is dependent on numerous factors including Qualification, Experience and Location.
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